/**
 * Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2025)
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

// Raw data (in Apache Arrow format) for a dataframe
// that uses a categorical column.
//
// df = pd.DataFrame([["foo", 100], ["bar", 200]], columns=["c1", "c2"])
// df["c1"] = df["c1"].astype("category")

export const CATEGORICAL_COLUMN = new Uint8Array([
  255, 255, 255, 255, 64, 3, 0, 0, 16, 0, 0, 0, 0, 0, 10, 0, 14, 0, 6, 0, 5, 0,
  8, 0, 10, 0, 0, 0, 0, 1, 4, 0, 16, 0, 0, 0, 0, 0, 10, 0, 12, 0, 0, 0, 4, 0,
  8, 0, 10, 0, 0, 0, 108, 2, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 12, 0, 0, 0, 8, 0,
  12, 0, 4, 0, 8, 0, 8, 0, 0, 0, 68, 2, 0, 0, 4, 0, 0, 0, 53, 2, 0, 0, 123, 34,
  105, 110, 100, 101, 120, 95, 99, 111, 108, 117, 109, 110, 115, 34, 58, 32,
  91, 123, 34, 107, 105, 110, 100, 34, 58, 32, 34, 114, 97, 110, 103, 101, 34,
  44, 32, 34, 110, 97, 109, 101, 34, 58, 32, 110, 117, 108, 108, 44, 32, 34,
  115, 116, 97, 114, 116, 34, 58, 32, 48, 44, 32, 34, 115, 116, 111, 112, 34,
  58, 32, 50, 44, 32, 34, 115, 116, 101, 112, 34, 58, 32, 49, 125, 93, 44, 32,
  34, 99, 111, 108, 117, 109, 110, 95, 105, 110, 100, 101, 120, 101, 115, 34,
  58, 32, 91, 123, 34, 110, 97, 109, 101, 34, 58, 32, 110, 117, 108, 108, 44,
  32, 34, 102, 105, 101, 108, 100, 95, 110, 97, 109, 101, 34, 58, 32, 110, 117,
  108, 108, 44, 32, 34, 112, 97, 110, 100, 97, 115, 95, 116, 121, 112, 101, 34,
  58, 32, 34, 117, 110, 105, 99, 111, 100, 101, 34, 44, 32, 34, 110, 117, 109,
  112, 121, 95, 116, 121, 112, 101, 34, 58, 32, 34, 111, 98, 106, 101, 99, 116,
  34, 44, 32, 34, 109, 101, 116, 97, 100, 97, 116, 97, 34, 58, 32, 123, 34,
  101, 110, 99, 111, 100, 105, 110, 103, 34, 58, 32, 34, 85, 84, 70, 45, 56,
  34, 125, 125, 93, 44, 32, 34, 99, 111, 108, 117, 109, 110, 115, 34, 58, 32,
  91, 123, 34, 110, 97, 109, 101, 34, 58, 32, 34, 99, 49, 34, 44, 32, 34, 102,
  105, 101, 108, 100, 95, 110, 97, 109, 101, 34, 58, 32, 34, 99, 49, 34, 44,
  32, 34, 112, 97, 110, 100, 97, 115, 95, 116, 121, 112, 101, 34, 58, 32, 34,
  99, 97, 116, 101, 103, 111, 114, 105, 99, 97, 108, 34, 44, 32, 34, 110, 117,
  109, 112, 121, 95, 116, 121, 112, 101, 34, 58, 32, 34, 105, 110, 116, 56, 34,
  44, 32, 34, 109, 101, 116, 97, 100, 97, 116, 97, 34, 58, 32, 123, 34, 110,
  117, 109, 95, 99, 97, 116, 101, 103, 111, 114, 105, 101, 115, 34, 58, 32, 50,
  44, 32, 34, 111, 114, 100, 101, 114, 101, 100, 34, 58, 32, 102, 97, 108, 115,
  101, 125, 125, 44, 32, 123, 34, 110, 97, 109, 101, 34, 58, 32, 34, 99, 50,
  34, 44, 32, 34, 102, 105, 101, 108, 100, 95, 110, 97, 109, 101, 34, 58, 32,
  34, 99, 50, 34, 44, 32, 34, 112, 97, 110, 100, 97, 115, 95, 116, 121, 112,
  101, 34, 58, 32, 34, 105, 110, 116, 54, 52, 34, 44, 32, 34, 110, 117, 109,
  112, 121, 95, 116, 121, 112, 101, 34, 58, 32, 34, 105, 110, 116, 54, 52, 34,
  44, 32, 34, 109, 101, 116, 97, 100, 97, 116, 97, 34, 58, 32, 110, 117, 108,
  108, 125, 93, 44, 32, 34, 99, 114, 101, 97, 116, 111, 114, 34, 58, 32, 123,
  34, 108, 105, 98, 114, 97, 114, 121, 34, 58, 32, 34, 112, 121, 97, 114, 114,
  111, 119, 34, 44, 32, 34, 118, 101, 114, 115, 105, 111, 110, 34, 58, 32, 34,
  49, 48, 46, 48, 46, 49, 34, 125, 44, 32, 34, 112, 97, 110, 100, 97, 115, 95,
  118, 101, 114, 115, 105, 111, 110, 34, 58, 32, 34, 49, 46, 53, 46, 51, 34,
  125, 0, 0, 0, 6, 0, 0, 0, 112, 97, 110, 100, 97, 115, 0, 0, 2, 0, 0, 0, 84,
  0, 0, 0, 20, 0, 0, 0, 16, 0, 20, 0, 8, 0, 6, 0, 7, 0, 12, 0, 0, 0, 16, 0, 16,
  0, 0, 0, 0, 0, 1, 2, 16, 0, 0, 0, 20, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0,
  0, 0, 99, 50, 0, 0, 176, 255, 255, 255, 0, 0, 0, 1, 64, 0, 0, 0, 16, 0, 24,
  0, 8, 0, 6, 0, 7, 0, 12, 0, 16, 0, 20, 0, 16, 0, 0, 0, 0, 0, 1, 5, 20, 0, 0,
  0, 64, 0, 0, 0, 28, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 99, 49, 0,
  0, 8, 0, 8, 0, 0, 0, 4, 0, 8, 0, 0, 0, 12, 0, 0, 0, 8, 0, 12, 0, 8, 0, 7, 0,
  8, 0, 0, 0, 0, 0, 0, 1, 8, 0, 0, 0, 4, 0, 4, 0, 4, 0, 0, 0, 255, 255, 255,
  255, 168, 0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 12, 0, 20, 0, 6, 0, 5, 0, 8, 0,
  12, 0, 12, 0, 0, 0, 0, 2, 4, 0, 20, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 8, 0,
  10, 0, 0, 0, 4, 0, 8, 0, 0, 0, 16, 0, 0, 0, 0, 0, 10, 0, 24, 0, 12, 0, 4, 0,
  8, 0, 10, 0, 0, 0, 76, 0, 0, 0, 16, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 3, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 98, 97, 114, 102, 111, 111,
  0, 0, 255, 255, 255, 255, 184, 0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 12, 0, 22,
  0, 6, 0, 5, 0, 8, 0, 12, 0, 12, 0, 0, 0, 0, 3, 4, 0, 24, 0, 0, 0, 24, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 10, 0, 24, 0, 12, 0, 4, 0, 8, 0, 10, 0, 0, 0, 92, 0, 0,
  0, 16, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0,
  0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0,
  16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
  0, 0, 0, 0, 0, 100, 0, 0, 0, 0, 0, 0, 0, 200, 0, 0, 0, 0, 0, 0, 0, 255, 255,
  255, 255, 0, 0, 0, 0,
])
